BSAGraphs
2024-05-09
df <- read_csv("./data/combinedstatshot.csv")## Rows: 165 Columns: 36
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (12): player, dunk_tot, dunk_pct, rim_tot, rim_pct, rim_asted, other2pt_...
## dbl (24): games, games_started, mp_per_g, fg_per_g, fga_per_g, fg_pct, fg2_p...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.df <- df |> select(!c("games_started", "pf_per_g"))
colnames(df)## [1] "player" "dunk_tot" "dunk_pct" "rim_tot"
## [5] "rim_pct" "rim_asted" "other2pt_tot" "other2pt_pct"
## [9] "other2pt_asted" "3pt_tot" "3pt_pct" "3pt_asted"
## [13] "games" "mp_per_g" "fg_per_g" "fga_per_g"
## [17] "fg_pct" "fg2_per_g" "fg2a_per_g" "fg2_pct"
## [21] "fg3_per_g" "fg3a_per_g" "fg3_pct" "ft_per_g"
## [25] "fta_per_g" "ft_pct" "orb_per_g" "drb_per_g"
## [29] "trb_per_g" "ast_per_g" "stl_per_g" "blk_per_g"
## [33] "tov_per_g" "pts_per_g"Plot 2pt attempts to 3pt attempts
From success script
path <- "~/BruinSports/data/draftdata.csv"
df_career_stats <- read_csv(path)## Rows: 960 Columns: 23
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): team_id, player, college_name, skip
## dbl (19): pick_overall, seasons, g, mp, pts, trb, ast, fg_pct, fg3_pct, ft_p...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.drop_cols <- c('team_id', 'skip', 'mp', 'pts', 'trb', 'ast')
df_career_stats <- df_career_stats |> select(!drop_cols)## Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
## ℹ Please use `all_of()` or `any_of()` instead.
## # Was:
## data %>% select(drop_cols)
##
## # Now:
## data %>% select(all_of(drop_cols))
##
## See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.df_lot_picks <- df_career_stats |> filter(pick_overall < 15)
colnames(df_career_stats)## [1] "pick_overall" "player" "college_name" "seasons" "g"
## [6] "fg_pct" "fg3_pct" "ft_pct" "mp_per_g" "pts_per_g"
## [11] "trb_per_g" "ast_per_g" "ws" "ws_per_48" "bpm"
## [16] "vorp" "year"# plotting the distribution of points + rebounds + asts (PRA) per game
df_lot_picks <- df_lot_picks |> mutate(
pra_per_g = pts_per_g + trb_per_g + ast_per_g,
pick_overall = factor(pick_overall)
)
# Group by draft position, and getting the mean
draft_means <- df_lot_picks |> group_by(pick_overall) |>
summarize(avg_mpg = mean(mp_per_g),
avg_ppg = mean(pts_per_g),
avg_trbpg = mean(trb_per_g),
avg_apg = mean(ast_per_g),
avg_prapg = mean(pra_per_g))
print(draft_means, n = 14)## # A tibble: 14 × 6
## pick_overall avg_mpg avg_ppg avg_trbpg avg_apg avg_prapg
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 31.2 18.8 6.52 4.32 29.7
## 2 2 26.9 13.7 4.96 2.88 21.6
## 3 3 30.0 17.6 6.3 3.52 27.4
## 4 4 27.5 12.8 5.39 2.48 20.7
## 5 5 26.0 12.7 4.92 3.55 21.2
## 6 6 23.2 10.2 4.35 2.26 16.8
## 7 7 27.6 13.4 4.88 2.77 21.0
## 8 8 21.1 8.82 3.37 1.77 14.0
## 9 9 24.5 10.9 4.55 2.52 18.0
## 10 10 23.5 10.4 3.61 2.31 16.3
## 11 11 21.1 10.2 3.69 2.16 16.0
## 12 12 24.6 10.6 4.47 2.23 17.3
## 13 13 22.6 10.9 3.99 2.23 17.1
## 14 14 20.3 8.78 3.75 1.3 13.8df_lot_picks |> ggplot(aes(x = pick_overall, y = pra_per_g)) +
geom_boxplot() +
labs(x = "Draft Pick", y = "Points-Rebounds-Assists Per Game")
Plotting the players in the top 25% in PRA per game
df2 <- left_join(df, df_lot_picks, by = c("player"), suffix = c("_college", "_nba"))
df2 <- df2 |>
separate_wider_delim(dunk_tot, delim = "-", names = c("dunk_made", "dunk_attempts"))
df2 <- df2 |>
separate_wider_delim(rim_tot, delim = "-", names = c("rim_made", "rim_attempts"))
df2 <- df2 |>
separate_wider_delim(other2pt_tot, delim = "-", names = c("other2pt_made", "other2pt_attempts"))
df2 <-df2 |>
mutate(across(c(dunk_made, dunk_attempts, rim_made, rim_attempts, other2pt_made, other2pt_attempts), as.numeric))defining bust metric
df2 <- df2 |> mutate(
vorp_per_g = vorp / g
)
df_top_players <- df2 |>
group_by(pick_overall) |>
summarize(across(c(pts_per_g_nba, trb_per_g_nba, ast_per_g_nba, pra_per_g, vorp_per_g), function(x) quantile(x, probs = 0.7)))
df_bottom_players <- df2 |>
group_by(pick_overall) |>
summarize(across(c(pts_per_g_nba, trb_per_g_nba, ast_per_g_nba, pra_per_g, vorp_per_g), function(x) quantile(x, probs = 0.3)))
# metric favors big men
# make the rebound percentile higher
is_not_bust <- function(pick_number, df_top_players = df_top_players, df2 = df2) {
ppg <- df_top_players |> pull(pts_per_g_nba)
apg <- df_top_players |> pull(ast_per_g_nba)
prapg <- df_top_players |> pull(pra_per_g)
df_top_rb <- df2 |>
group_by(pick_overall) |>
summarize(trb_per_g = quantile(trb_per_g_nba, probs = 0.8))
rpg <- df_top_rb |> pull(trb_per_g)
df_top_vorp <- df2 |>
group_by(pick_overall) |>
summarize(vorp_per_g = median(vorp_per_g))
vorppg <- df_top_vorp |> pull(vorp_per_g)
df <- df2 |> filter(pick_overall == pick_number) |>
filter(pra_per_g >= prapg[[pick_number]] | ast_per_g_nba >= apg[[pick_number]] | trb_per_g_nba >= rpg[[pick_number]] | pts_per_g_nba >= ppg[[pick_number]]) |>
filter(vorp_per_g >= vorppg[[pick_number]])
# must also have played at least most of their career in the nba
df <- df |> filter(seasons >= 4 / 5 * (2023-year))
return(df)
}
is_bust <- function(pick_number, df_bottom_players = df_bottom_players, df2 = df2) {
ppg <- df_bottom_players |> pull(pts_per_g_nba)
apg <- df_bottom_players |> pull(ast_per_g_nba)
prapg <- df_bottom_players |> pull(pra_per_g)
df_bottom_rb <- df2 |>
group_by(pick_overall) |>
summarize(trb_per_g = quantile(trb_per_g_nba, probs = 0.4))
rpg <- df_bottom_rb |> pull(trb_per_g)
df_top_vorp <- df2 |>
group_by(pick_overall) |>
summarize(vorp_per_g = median(vorp_per_g))
vorppg <- df_top_vorp |> pull(vorp_per_g)
# playing less than half the seasons since drafted makes you a bust
df <- df2 |> filter(pick_overall == pick_number) |>
filter((pra_per_g < prapg[[pick_number]] & ast_per_g_nba < apg[[pick_number]] & trb_per_g_nba < rpg[[pick_number]] & pts_per_g_nba < ppg[[pick_number]] & vorp_per_g < vorppg[[pick_number]]) | seasons < 1 / 2 * (2023-year))
return(df)
}# first overall picks (not bust)
df_pick_1 <- is_not_bust(1, df_top_players, df2)
# first overall picks (bust)
df_pick_1_bust <- is_bust(1, df_bottom_players, df2)
df_pick_1## # A tibble: 7 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 John Wall 33 36 91.7% 116 182 63.7%
## 2 Kyrie Irving 0 0 0% 26 39 66.7%
## 3 Anthony Davis 96 98 98.0% 152 174 87.4%
## 4 Karl-Anthony T… 22 24 91.7% 87 121 71.9%
## 5 Ben Simmons 56 61 91.8% 159 220 72.3%
## 6 Zion Williamson 72 79 91.1% 247 313 78.9%
## 7 Anthony Edwards 27 27 100.0% 89 129 69.0%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …df_pick_1_bust## # A tibble: 1 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Anthony Bennett 53 58 91.4% 100 140 71.4%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …# second overall
df_pick_2 <- is_not_bust(2, df_top_players, df2)
df_pick_2_bust <- is_bust(2, df_bottom_players, df2)
df_pick_2## # A tibble: 5 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 D'Angelo Russe… 4 4 100.0% 70 110 63.6%
## 2 Brandon Ingram 17 17 100.0% 69 117 59.0%
## 3 Lonzo Ball 37 40 92.5% 94 120 78.3%
## 4 Ja Morant 28 31 90.3% 160 264 60.6%
## 5 Chet Holmgren 57 57 100.0% 105 125 84.0%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …df_pick_2_bust## # A tibble: 1 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Derrick Willia… 56 60 93.3% 135 188 71.8%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …# third overall
df_pick_3 <- is_not_bust(3, df_top_players, df2)
df_pick_3_bust <- is_bust(3, df_bottom_players, df2)
df_pick_3## # A tibble: 4 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Bradley Beal 18 20 90.0% 89 137 65.0%
## 2 Joel Embiid 30 30 100.0% 80 99 80.8%
## 3 Jayson Tatum 18 21 85.7% 79 126 62.7%
## 4 Evan Mobley 63 66 95.5% 113 144 78.5%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …df_pick_3_bust## # A tibble: 1 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Jahlil Okafor 64 67 95.5% 213 270 78.9%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …# fourth overall
df_pick_4 <- is_not_bust(4, df_top_players, df2)
df_pick_4_bust <- is_bust(4, df_bottom_players, df2)
df_pick_4## # A tibble: 4 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Aaron Gordon 54 56 96.4% 137 198 69.2%
## 2 Jaren Jackson … 31 31 100.0% 61 93 65.6%
## 3 Scottie Barnes 19 21 90.5% 61 89 68.5%
## 4 Keegan Murray 63 67 94.0% 196 277 70.8%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …df_pick_4_bust## # A tibble: 0 × 55
## # ℹ 55 variables: player <chr>, dunk_made <dbl>, dunk_attempts <dbl>,
## # dunk_pct <chr>, rim_made <dbl>, rim_attempts <dbl>, rim_pct <chr>,
## # rim_asted <chr>, other2pt_made <dbl>, other2pt_attempts <dbl>,
## # other2pt_pct <chr>, other2pt_asted <chr>, 3pt_tot <chr>, 3pt_pct <chr>,
## # 3pt_asted <chr>, games <dbl>, mp_per_g_college <dbl>, fg_per_g <dbl>,
## # fga_per_g <dbl>, fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>,
## # fg2_pct <dbl>, fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, …# fifth overall
df_pick_5 <- is_not_bust(5, df_top_players, df2)
df_pick_5_bust <- is_bust(5, df_bottom_players, df2)
df_pick_5## # A tibble: 3 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 DeMarcus Cousi… 53 57 93.0% 144 189 76.2%
## 2 De'Aaron Fox 20 21 95.2% 131 203 64.5%
## 3 Trae Young 0 0 0% 105 201 52.2%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …df_pick_5_bust## # A tibble: 1 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Thomas Robinson 70 83 84.3% 169 262 64.5%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …# sixth overall
df_pick_6 <- is_not_bust(6, df_top_players, df2)
df_pick_6_bust <- is_bust(6, df_bottom_players, df2)
df_pick_6## # A tibble: 5 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Damian Lillard 13 17 76.5% 98 169 58.0%
## 2 Nerlens Noel 48 50 96.0% 76 99 76.8%
## 3 Marcus Smart 16 18 88.9% 78 110 70.9%
## 4 Buddy Hield 18 22 81.8% 119 178 66.9%
## 5 Onyeka Okongwu 58 61 95.1% 135 186 72.6%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …df_pick_6_bust## # A tibble: 1 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Ekpe Udoh 30 32 93.8% 78 109 71.6%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …# seventh overall
df_pick_7 <- is_not_bust(7, df_top_players, df2)
df_pick_7_bust <- is_bust(7, df_bottom_players, df2)
df_pick_7## # A tibble: 3 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Julius Randle 37 40 92.5% 132 197 67.0%
## 2 Jamal Murray 18 19 94.7% 77 111 69.4%
## 3 Lauri Markkanen 20 24 83.3% 65 100 65.0%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …df_pick_7_bust## # A tibble: 1 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Ben McLemore 44 45 97.8% 90 126 71.4%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …# eighth overall
df_pick_8 <- is_not_bust(8, df_top_players, df2)
df_pick_8_bust <- is_bust(8, df_bottom_players, df2)
df_pick_8## # A tibble: 3 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Al-Farouq Aminu 46 48 95.8% 112 173 64.7%
## 2 Kentavious Cal… 15 16 93.8% 63 94 67.0%
## 3 Franz Wagner 11 11 100.0% 63 93 67.7%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …df_pick_8_bust## # A tibble: 0 × 55
## # ℹ 55 variables: player <chr>, dunk_made <dbl>, dunk_attempts <dbl>,
## # dunk_pct <chr>, rim_made <dbl>, rim_attempts <dbl>, rim_pct <chr>,
## # rim_asted <chr>, other2pt_made <dbl>, other2pt_attempts <dbl>,
## # other2pt_pct <chr>, other2pt_asted <chr>, 3pt_tot <chr>, 3pt_pct <chr>,
## # 3pt_asted <chr>, games <dbl>, mp_per_g_college <dbl>, fg_per_g <dbl>,
## # fga_per_g <dbl>, fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>,
## # fg2_pct <dbl>, fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, …# ninth overall
df_pick_9 <- is_not_bust(9, df_top_players, df2)
df_pick_9_bust <- is_bust(9, df_bottom_players, df2)
df_pick_9## # A tibble: 5 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Gordon Hayward 19 20 95.0% 89 128 69.5%
## 2 Kemba Walker 3 3 100.0% 115 196 58.7%
## 3 Andre Drummond 80 89 89.9% 130 185 70.3%
## 4 Trey Burke 9 9 100.0% 67 105 63.8%
## 5 Jakob Poeltl 32 34 94.1% 199 284 70.1%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …df_pick_9_bust## # A tibble: 1 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Kevin Knox 18 20 90.0% 65 99 65.7%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …# tenth overall
df_pick_10 <- is_not_bust(10, df_top_players, df2)
df_pick_10_bust <- is_bust(10, df_bottom_players, df2)
df_pick_10## # A tibble: 5 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Paul George 18 22 81.8% 70 106 66.0%
## 2 CJ McCollum 3 3 100.0% 34 63 54.0%
## 3 Elfrid Payton 21 24 87.5% 169 247 68.4%
## 4 Mikal Bridges 35 42 83.3% 109 161 67.7%
## 5 Jalen Smith 49 52 94.2% 114 158 72.2%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …df_pick_10_bust## # A tibble: 2 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Ziaire Williams 10 11 90.9% 26 49 53.1%
## 2 Johnny Davis 16 19 84.2% 89 143 62.2%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …# eleventh overall
df_pick_11 <- is_not_bust(11, df_top_players, df2)
df_pick_11_bust <- is_bust(11, df_bottom_players, df2)
df_pick_11## # A tibble: 4 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Klay Thompson 8 8 100.0% 66 110 60.0%
## 2 Myles Turner 11 13 84.6% 40 54 74.1%
## 3 Domantas Sabon… 22 24 91.7% 157 214 73.4%
## 4 Shai Gilgeous-… 11 11 100.0% 108 182 59.3%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …df_pick_11_bust## # A tibble: 2 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 James Bouknight 12 12 100.0% 52 79 65.8%
## 2 Jett Howard 6 6 100.0% 29 47 61.7%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …# twelveth overall
df_pick_12 <- is_not_bust(12, df_top_players, df2)
df_pick_12_bust <- is_bust(12, df_bottom_players, df2)
df_pick_12## # A tibble: 5 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Steven Adams 29 33 87.9% 85 129 65.9%
## 2 Miles Bridges 30 35 85.7% 84 128 65.6%
## 3 Tyrese Halibur… 7 8 87.5% 46 62 74.2%
## 4 Jalen Williams 25 27 92.6% 124 186 66.7%
## 5 Dereck Lively … 54 55 98.2% 74 96 77.1%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …df_pick_12_bust## # A tibble: 1 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Xavier Henry 17 17 100.0% 60 90 66.7%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …# thirteenth overall
df_pick_13 <- is_not_bust(13, df_top_players, df2)
df_pick_13_bust <- is_bust(13, df_bottom_players, df2)
df_pick_13## # A tibble: 7 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Ed Davis 26 27 96.3% 42 50 84.0%
## 2 Kelly Olynyk 25 28 89.3% 152 212 71.7%
## 3 Zach LaVine 21 25 84.0% 51 90 56.7%
## 4 Devin Booker 8 9 88.9% 42 59 71.2%
## 5 Donovan Mitche… 9 13 69.2% 64 116 55.2%
## 6 Tyler Herro 4 5 80.0% 56 84 66.7%
## 7 Jalen Duren 70 76 92.1% 111 152 73.0%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …df_pick_13_bust## # A tibble: 2 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Kendall Marsha… 0 0 0% 35 53 66.0%
## 2 Jerome Robinson 12 13 92.3% 98 157 62.4%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …# fourteenth overall
df_pick_14 <- is_not_bust(14, df_top_players, df2)
df_pick_14_bust <- is_bust(14, df_bottom_players, df2)
df_pick_14## # A tibble: 4 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Marcus Morris 31 33 93.9% 114 147 77.6%
## 2 T.J. Warren 37 37 100.0% 192 251 76.5%
## 3 Cameron Payne 3 3 100.0% 53 87 60.9%
## 4 Bam Adebayo 99 105 94.3% 138 185 74.6%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …df_pick_14_bust## # A tibble: 1 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Romeo Langford 10 14 71.4% 91 138 65.9%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …# combining dataframes
df_good <- bind_rows(df_pick_1, df_pick_2, df_pick_3, df_pick_4, df_pick_5, df_pick_6, df_pick_7, df_pick_8, df_pick_9,
df_pick_10, df_pick_11, df_pick_12, df_pick_13, df_pick_14)
df_busts <- bind_rows(df_pick_1_bust, df_pick_2_bust, df_pick_3_bust, df_pick_4_bust, df_pick_5_bust, df_pick_6_bust, df_pick_7_bust, df_pick_8_bust, df_pick_9_bust, df_pick_10_bust, df_pick_11_bust, df_pick_12_bust, df_pick_13_bust, df_pick_14_bust)
print(df_good, n = 20)## # A tibble: 64 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 John Wall 33 36 91.7% 116 182 63.7%
## 2 Kyrie Irving 0 0 0% 26 39 66.7%
## 3 Anthony Davis 96 98 98.0% 152 174 87.4%
## 4 Karl-Anthony … 22 24 91.7% 87 121 71.9%
## 5 Ben Simmons 56 61 91.8% 159 220 72.3%
## 6 Zion Williams… 72 79 91.1% 247 313 78.9%
## 7 Anthony Edwar… 27 27 100.0% 89 129 69.0%
## 8 D'Angelo Russ… 4 4 100.0% 70 110 63.6%
## 9 Brandon Ingram 17 17 100.0% 69 117 59.0%
## 10 Lonzo Ball 37 40 92.5% 94 120 78.3%
## 11 Ja Morant 28 31 90.3% 160 264 60.6%
## 12 Chet Holmgren 57 57 100.0% 105 125 84.0%
## 13 Bradley Beal 18 20 90.0% 89 137 65.0%
## 14 Joel Embiid 30 30 100.0% 80 99 80.8%
## 15 Jayson Tatum 18 21 85.7% 79 126 62.7%
## 16 Evan Mobley 63 66 95.5% 113 144 78.5%
## 17 Aaron Gordon 54 56 96.4% 137 198 69.2%
## 18 Jaren Jackson… 31 31 100.0% 61 93 65.6%
## 19 Scottie Barnes 19 21 90.5% 61 89 68.5%
## 20 Keegan Murray 63 67 94.0% 196 277 70.8%
## # ℹ 44 more rows
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>, …df_good |> pull(player)## [1] "John Wall" "Kyrie Irving"
## [3] "Anthony Davis" "Karl-Anthony Towns"
## [5] "Ben Simmons" "Zion Williamson"
## [7] "Anthony Edwards" "D'Angelo Russell"
## [9] "Brandon Ingram" "Lonzo Ball"
## [11] "Ja Morant" "Chet Holmgren"
## [13] "Bradley Beal" "Joel Embiid"
## [15] "Jayson Tatum" "Evan Mobley"
## [17] "Aaron Gordon" "Jaren Jackson Jr."
## [19] "Scottie Barnes" "Keegan Murray"
## [21] "DeMarcus Cousins" "De'Aaron Fox"
## [23] "Trae Young" "Damian Lillard"
## [25] "Nerlens Noel" "Marcus Smart"
## [27] "Buddy Hield" "Onyeka Okongwu"
## [29] "Julius Randle" "Jamal Murray"
## [31] "Lauri Markkanen" "Al-Farouq Aminu"
## [33] "Kentavious Caldwell-Pope" "Franz Wagner"
## [35] "Gordon Hayward" "Kemba Walker"
## [37] "Andre Drummond" "Trey Burke"
## [39] "Jakob Poeltl" "Paul George"
## [41] "CJ McCollum" "Elfrid Payton"
## [43] "Mikal Bridges" "Jalen Smith"
## [45] "Klay Thompson" "Myles Turner"
## [47] "Domantas Sabonis" "Shai Gilgeous-Alexander"
## [49] "Steven Adams" "Miles Bridges"
## [51] "Tyrese Haliburton" "Jalen Williams"
## [53] "Dereck Lively II" "Ed Davis"
## [55] "Kelly Olynyk" "Zach LaVine"
## [57] "Devin Booker" "Donovan Mitchell"
## [59] "Tyler Herro" "Jalen Duren"
## [61] "Marcus Morris" "T.J. Warren"
## [63] "Cameron Payne" "Bam Adebayo"df_busts## # A tibble: 15 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Anthony Benne… 53 58 91.4% 100 140 71.4%
## 2 Derrick Willi… 56 60 93.3% 135 188 71.8%
## 3 Jahlil Okafor 64 67 95.5% 213 270 78.9%
## 4 Thomas Robins… 70 83 84.3% 169 262 64.5%
## 5 Ekpe Udoh 30 32 93.8% 78 109 71.6%
## 6 Ben McLemore 44 45 97.8% 90 126 71.4%
## 7 Kevin Knox 18 20 90.0% 65 99 65.7%
## 8 Ziaire Willia… 10 11 90.9% 26 49 53.1%
## 9 Johnny Davis 16 19 84.2% 89 143 62.2%
## 10 James Bouknig… 12 12 100.0% 52 79 65.8%
## 11 Jett Howard 6 6 100.0% 29 47 61.7%
## 12 Xavier Henry 17 17 100.0% 60 90 66.7%
## 13 Kendall Marsh… 0 0 0% 35 53 66.0%
## 14 Jerome Robins… 12 13 92.3% 98 157 62.4%
## 15 Romeo Langford 10 14 71.4% 91 138 65.9%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …df_busts |> pull(player)## [1] "Anthony Bennett" "Derrick Williams" "Jahlil Okafor" "Thomas Robinson"
## [5] "Ekpe Udoh" "Ben McLemore" "Kevin Knox" "Ziaire Williams"
## [9] "Johnny Davis" "James Bouknight" "Jett Howard" "Xavier Henry"
## [13] "Kendall Marshall" "Jerome Robinson" "Romeo Langford"plot_good <- ggplot(df_good, aes(x = fg2_per_g, y = fg3_per_g)) +
geom_point(color = "green", size = 4, alpha = 0.5) +
geom_label_repel(data = df_good, aes(label = player), size = 1.5, max.overlaps = 20) +
labs(title = "CBB Shot Selection for Good Value NBA Lottery Picks", x = "2PT Field Goal Makes per game", y = "3PT Field Goal Makes per game") +
theme_bw()
plot_busts <- ggplot(df_busts, aes(x = fg2_per_g, y = fg3_per_g, label = player)) +
geom_point(color = "red", size = 4, alpha = 0.5) +
geom_label_repel(size = 3) +
labs(title = "CBB Shot Selection for NBA Lottery Busts", x = "2PT Field Goal Makes per game", y = "3PT Field Goal Makes per game") +
theme_bw()
plot_combined <- ggplot(df2, aes(x = fg2_per_g, y = fg3_per_g)) +
geom_point(aes(color = "Average value"), size = 4, alpha = 0.2) +
geom_point(data = df_busts, aes(color = "Bad value"), size = 4, alpha = 0.5) +
geom_point(data = df_good, aes(color = "Good value"), size = 4, alpha = 0.5) +
labs(title = "CBB Shot Selection for NBA Lottery Picks",
x = "2PT Field Goal Makes per game", y = "3PT Field Goal Makes per game") +
scale_color_manual(values = c("Bad value" = "red", "Good value" = "green", "Average value" = "grey")) +
theme_bw()
plot_combined
plot_busts
plot_good
Principal Component Analysis of college stats, for grouping/covariance
library(corrr)
library(ggcorrplot)
library(FactoMineR)
library(factoextra)## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBaPreparing Data
colnames(df2)## [1] "player" "dunk_made" "dunk_attempts"
## [4] "dunk_pct" "rim_made" "rim_attempts"
## [7] "rim_pct" "rim_asted" "other2pt_made"
## [10] "other2pt_attempts" "other2pt_pct" "other2pt_asted"
## [13] "3pt_tot" "3pt_pct" "3pt_asted"
## [16] "games" "mp_per_g_college" "fg_per_g"
## [19] "fga_per_g" "fg_pct_college" "fg2_per_g"
## [22] "fg2a_per_g" "fg2_pct" "fg3_per_g"
## [25] "fg3a_per_g" "fg3_pct_college" "ft_per_g"
## [28] "fta_per_g" "ft_pct_college" "orb_per_g"
## [31] "drb_per_g" "trb_per_g_college" "ast_per_g_college"
## [34] "stl_per_g" "blk_per_g" "tov_per_g"
## [37] "pts_per_g_college" "pick_overall" "college_name"
## [40] "seasons" "g" "fg_pct_nba"
## [43] "fg3_pct_nba" "ft_pct_nba" "mp_per_g_nba"
## [46] "pts_per_g_nba" "trb_per_g_nba" "ast_per_g_nba"
## [49] "ws" "ws_per_48" "bpm"
## [52] "vorp" "year" "pra_per_g"
## [55] "vorp_per_g"df3 <- column_to_rownames(df2, var = "player")
df_cbb <- df3 |> select(
dunk_made, dunk_attempts, dunk_pct, rim_made, rim_attempts, rim_pct, rim_asted,
other2pt_made, other2pt_attempts, other2pt_pct, other2pt_asted, fg2_pct, fg3_per_g, fg3a_per_g,
`3pt_asted`, games, ft_per_g, fta_per_g, ast_per_g_college, orb_per_g, drb_per_g,
stl_per_g, blk_per_g, tov_per_g, pts_per_g_college
) |> rename(fg3_asted = `3pt_asted`) |>
mutate(dunk_pct = parse_number(dunk_pct) / 100, # changing to percentages
rim_pct = parse_number(rim_pct) / 100,
rim_asted = parse_number(rim_asted) / 100,
other2pt_pct = parse_number(other2pt_pct) / 100,
other2pt_asted = parse_number(other2pt_asted) / 100,
fg3_asted = parse_number(fg3_asted) / 100,
fg3_pct_per_g = fg3_per_g / fg3a_per_g)
df_cbb <- df_cbb |>
mutate(fg3_pct_per_g = coalesce(fg3_pct_per_g, 0)) |>
relocate(fg3_pct_per_g, .after = fg3_asted)
to_per_game <- function(x, games) {
x <- x / games
return(x)
}
df_cbb <- df_cbb |> # making everything per game
mutate(across(c(dunk_made, dunk_attempts, rim_made, rim_attempts, other2pt_made,
other2pt_attempts), function(x) to_per_game(x, games)))
colSums(is.na(df_cbb))## dunk_made dunk_attempts dunk_pct rim_made
## 0 0 0 0
## rim_attempts rim_pct rim_asted other2pt_made
## 0 0 0 0
## other2pt_attempts other2pt_pct other2pt_asted fg2_pct
## 0 0 0 0
## fg3_per_g fg3a_per_g fg3_asted fg3_pct_per_g
## 0 0 0 0
## games ft_per_g fta_per_g ast_per_g_college
## 0 0 0 0
## orb_per_g drb_per_g stl_per_g blk_per_g
## 0 0 0 0
## tov_per_g pts_per_g_college
## 0 0Following this guide: https://www.datacamp.com/tutorial/pca-analysis-r
df_cbb_scaled <- as_tibble(scale(df_cbb |> select(-games)))
df_cbb_scaled## # A tibble: 165 × 25
## dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct rim_asted
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.360 0.381 0.147 0.898 1.16 -0.569 -0.927
## 2 -0.777 -0.801 0.334 -0.908 -1.09 1.49 -1.46
## 3 1.28 1.26 0.328 1.47 1.12 1.46 0.671
## 4 0.623 0.680 0.0540 0.492 0.395 0.600 0.742
## 5 1.16 1.17 0.228 1.45 1.19 1.17 0.642
## 6 0.267 0.254 0.278 0.0749 -0.0119 0.531 -0.0603
## 7 -0.546 -0.575 0.384 0.247 0.380 -0.430 0.0517
## 8 0.121 0.0815 0.403 -0.231 -0.159 -0.430 0.0753
## 9 -0.622 -0.648 0.353 -0.670 -0.737 0.238 0.624
## 10 -0.606 -0.559 -0.469 -0.822 -0.844 -0.249 -0.615
## # ℹ 155 more rows
## # ℹ 18 more variables: other2pt_made <dbl>, other2pt_attempts <dbl>,
## # other2pt_pct <dbl>, other2pt_asted <dbl>, fg2_pct <dbl>, fg3_per_g <dbl>,
## # fg3a_per_g <dbl>, fg3_asted <dbl>, fg3_pct_per_g <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ast_per_g_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>,
## # stl_per_g <dbl>, blk_per_g <dbl>, tov_per_g <dbl>, pts_per_g_college <dbl># df_cbb_scaled$player <- df_cbb$player
# df_cbb_scaled <- df_cbb_scaled |> relocate(player, .before = dunk_made)
corr_matrix <- cor(df_cbb_scaled)
ggcorrplot(corr_matrix, method = "square")
ggcorrplot(corr_matrix, method = "square", hc.order = TRUE,
type = "lower")
pca <- princomp(corr_matrix)
summary(pca)## Importance of components:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5
## Standard deviation 1.5264968 0.8275209 0.38297252 0.26189097 0.2392287
## Proportion of Variance 0.6731171 0.1978139 0.04236762 0.01981253 0.0165320
## Cumulative Proportion 0.6731171 0.8709310 0.91329860 0.93311112 0.9496431
## Comp.6 Comp.7 Comp.8 Comp.9 Comp.10
## Standard deviation 0.2338592 0.19672875 0.166119165 0.136620686 0.092515541
## Proportion of Variance 0.0157982 0.01117981 0.007971468 0.005391775 0.002472454
## Cumulative Proportion 0.9654413 0.97662114 0.984592603 0.989984378 0.992456832
## Comp.11 Comp.12 Comp.13 Comp.14
## Standard deviation 0.087727419 0.077813416 0.063038666 0.0522112215
## Proportion of Variance 0.002223154 0.001749072 0.001147923 0.0007874564
## Cumulative Proportion 0.994679986 0.996429058 0.997576981 0.9983644376
## Comp.15 Comp.16 Comp.17 Comp.18
## Standard deviation 0.0466045703 0.0419416771 0.0315945772 0.0227293361
## Proportion of Variance 0.0006274163 0.0005081482 0.0002883526 0.0001492356
## Cumulative Proportion 0.9989918540 0.9995000022 0.9997883548 0.9999375904
## Comp.19 Comp.20 Comp.21 Comp.22
## Standard deviation 1.371363e-02 4.393920e-03 2.341039e-03 1.590896e-03
## Proportion of Variance 5.432546e-05 5.577032e-06 1.583129e-06 7.311092e-07
## Cumulative Proportion 9.999919e-01 9.999975e-01 9.999991e-01 9.999998e-01
## Comp.23 Comp.24 Comp.25
## Standard deviation 7.221375e-04 3.825110e-04 7.167572e-09
## Proportion of Variance 1.506394e-07 4.226557e-08 1.484031e-17
## Cumulative Proportion 1.000000e+00 1.000000e+00 1.000000e+00fviz_eig(pca, addlabels = TRUE, title = "Principal Components Scree Plot")
fviz_pca_var(pca, col.var = "cos2", repel = TRUE, title = "Contributions of Variables to Components 1 and 2", gradient.cols = c("lightblue", "black"))
fviz_cos2(pca, choice = "var", axes = 1:2)
Kmeans Clustering: https://medium.com/@zullinira23/implementation-of-principal-component-analysis-pca-on-k-means-clustering-in-r-794f03ec15f
df_cbb.sample <- df_cbb |> sample_frac(1, replace = FALSE)
head(df_cbb.sample)## dunk_made dunk_attempts dunk_pct rim_made
## Dion Waiters 0.3661972 0.4084507 0.897 1.2676056
## Buddy Hield 0.1363636 0.1666667 0.818 0.9015152
## Kentavious Caldwell-Pope 0.2343750 0.2500000 0.938 0.9843750
## Jalen Williams 0.2976190 0.3214286 0.926 1.4761905
## Dennis Smith Jr. 0.6875000 0.6875000 1.000 3.2187500
## Ochai Agbaji 0.3032787 0.3360656 0.902 0.9754098
## rim_attempts rim_pct rim_asted other2pt_made
## Dion Waiters 2.028169 0.625 0.378 0.5492958
## Buddy Hield 1.348485 0.669 0.277 0.2651515
## Kentavious Caldwell-Pope 1.468750 0.670 0.349 0.6093750
## Jalen Williams 2.214286 0.667 0.355 0.6309524
## Dennis Smith Jr. 5.031250 0.640 0.155 1.0312500
## Ochai Agbaji 1.344262 0.726 0.529 0.2950820
## other2pt_attempts other2pt_pct other2pt_asted fg2_pct
## Dion Waiters 1.4084507 0.390 0.282 0.513
## Buddy Hield 0.7651515 0.347 0.057 0.508
## Kentavious Caldwell-Pope 1.7500000 0.348 0.385 0.504
## Jalen Williams 1.6071429 0.393 0.170 0.525
## Dennis Smith Jr. 3.3437500 0.308 0.030 0.509
## Ochai Agbaji 1.0327869 0.286 0.389 0.526
## fg3_per_g fg3a_per_g fg3_asted fg3_pct_per_g games
## Dion Waiters 1.0 2.8 0.780 0.3571429 71
## Buddy Hield 2.6 6.8 0.687 0.3823529 132
## Kentavious Caldwell-Pope 2.3 6.9 0.583 0.3333333 64
## Jalen Williams 1.1 3.0 0.714 0.3666667 84
## Dennis Smith Jr. 1.7 4.8 0.418 0.3541667 32
## Ochai Agbaji 2.0 5.5 0.854 0.3636364 122
## ft_per_g fta_per_g ast_per_g_college orb_per_g
## Dion Waiters 1.8 2.3 2.0 0.4
## Buddy Hield 2.8 3.4 1.9 1.6
## Kentavious Caldwell-Pope 2.9 3.9 1.5 1.3
## Jalen Williams 2.6 3.3 2.9 0.5
## Dennis Smith Jr. 4.5 6.3 6.2 0.8
## Ochai Agbaji 1.8 2.5 1.6 1.0
## drb_per_g stl_per_g blk_per_g tov_per_g
## Dion Waiters 1.5 1.5 0.2 1.1
## Buddy Hield 3.4 1.3 0.3 2.2
## Kentavious Caldwell-Pope 4.8 1.9 0.4 1.6
## Jalen Williams 3.2 1.2 0.5 1.6
## Dennis Smith Jr. 3.8 1.9 0.4 3.4
## Ochai Agbaji 3.4 1.0 0.5 1.7
## pts_per_g_college
## Dion Waiters 9.7
## Buddy Hield 17.4
## Kentavious Caldwell-Pope 15.8
## Jalen Williams 12.6
## Dennis Smith Jr. 18.1
## Ochai Agbaji 13.5df_cbb.pca <- PCA(df_cbb.sample |> select(-games), scale.unit = TRUE, graph = FALSE)
fviz_eig(df_cbb.pca, addlabels = TRUE)
fviz_pca_var(df_cbb.pca, col.var = "cos2", gradient.cols = c("lightblue", "black"), repel = TRUE)
var <- get_pca_var(df_cbb.pca)
fviz_cos2(df_cbb.pca, choice = "var", axes = 1:2)
fviz_pca_ind(df_cbb.pca, repel = TRUE, labelsize = 1)
summary(df_cbb.pca)##
## Call:
## PCA(X = select(df_cbb.sample, -games), scale.unit = TRUE, graph = FALSE)
##
##
## Eigenvalues
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6 Dim.7
## Variance 7.910 5.334 2.090 1.463 1.278 1.196 1.058
## % of var. 31.642 21.336 8.359 5.852 5.113 4.784 4.231
## Cumulative % of var. 31.642 52.978 61.336 67.189 72.302 77.085 81.316
## Dim.8 Dim.9 Dim.10 Dim.11 Dim.12 Dim.13 Dim.14
## Variance 0.980 0.728 0.551 0.447 0.419 0.332 0.269
## % of var. 3.922 2.911 2.203 1.787 1.677 1.329 1.075
## Cumulative % of var. 85.238 88.149 90.352 92.140 93.817 95.146 96.221
## Dim.15 Dim.16 Dim.17 Dim.18 Dim.19 Dim.20 Dim.21
## Variance 0.254 0.210 0.164 0.138 0.072 0.062 0.022
## % of var. 1.017 0.840 0.655 0.553 0.286 0.247 0.088
## Cumulative % of var. 97.238 98.079 98.734 99.287 99.574 99.821 99.909
## Dim.22 Dim.23 Dim.24 Dim.25
## Variance 0.012 0.006 0.004 0.002
## % of var. 0.046 0.023 0.014 0.007
## Cumulative % of var. 99.955 99.978 99.993 100.000
##
## Individuals (the 10 first)
## Dist Dim.1 ctr cos2 Dim.2 ctr cos2
## Dion Waiters | 4.206 | -1.746 0.234 0.172 | -3.003 1.024 0.510
## Buddy Hield | 4.345 | -3.249 0.809 0.559 | -1.080 0.132 0.062
## Kentavious Caldwell-Pope | 3.786 | -2.159 0.357 0.325 | -0.907 0.093 0.057
## Jalen Williams | 2.683 | -1.639 0.206 0.373 | -1.607 0.293 0.359
## Dennis Smith Jr. | 5.454 | -2.630 0.530 0.233 | 3.845 1.679 0.497
## Ochai Agbaji | 3.950 | -1.316 0.133 0.111 | -2.815 0.901 0.508
## Bradley Beal | 2.277 | -0.914 0.064 0.161 | 0.487 0.027 0.046
## Dereck Lively II | 8.949 | 5.280 2.136 0.348 | -4.948 2.782 0.306
## Greg Monroe | 3.685 | 0.629 0.030 0.029 | 0.824 0.077 0.050
## Brandon Ingram | 3.997 | -1.765 0.239 0.195 | 1.668 0.316 0.174
## Dim.3 ctr cos2
## Dion Waiters | -0.643 0.120 0.023 |
## Buddy Hield | -0.500 0.073 0.013 |
## Kentavious Caldwell-Pope | -0.034 0.000 0.000 |
## Jalen Williams | -0.587 0.100 0.048 |
## Dennis Smith Jr. | -2.043 1.210 0.140 |
## Ochai Agbaji | 0.036 0.000 0.000 |
## Bradley Beal | -0.205 0.012 0.008 |
## Dereck Lively II | -1.268 0.466 0.020 |
## Greg Monroe | -1.667 0.806 0.205 |
## Brandon Ingram | 2.167 1.362 0.294 |
##
## Variables (the 10 first)
## Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3
## dunk_made | 0.834 8.788 0.695 | 0.336 2.113 0.113 | -0.023
## dunk_attempts | 0.828 8.662 0.685 | 0.337 2.132 0.114 | -0.023
## dunk_pct | 0.343 1.489 0.118 | -0.070 0.091 0.005 | 0.214
## rim_made | 0.585 4.330 0.343 | 0.673 8.503 0.454 | -0.093
## rim_attempts | 0.453 2.598 0.205 | 0.740 10.256 0.547 | -0.097
## rim_pct | 0.728 6.706 0.530 | -0.141 0.373 0.020 | -0.002
## rim_asted | 0.768 7.456 0.590 | -0.352 2.327 0.124 | 0.180
## other2pt_made | 0.109 0.149 0.012 | 0.603 6.823 0.364 | 0.563
## other2pt_attempts | 0.102 0.132 0.010 | 0.651 7.952 0.424 | 0.490
## other2pt_pct | 0.015 0.003 0.000 | -0.039 0.029 0.002 | 0.358
## ctr cos2
## dunk_made 0.025 0.001 |
## dunk_attempts 0.025 0.001 |
## dunk_pct 2.193 0.046 |
## rim_made 0.416 0.009 |
## rim_attempts 0.448 0.009 |
## rim_pct 0.000 0.000 |
## rim_asted 1.558 0.033 |
## other2pt_made 15.195 0.318 |
## other2pt_attempts 11.501 0.240 |
## other2pt_pct 6.122 0.128 |pca2 <- prcomp(df_cbb.sample, center = TRUE, scale = TRUE)
summary(pca2)## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 2.8233 2.3564 1.45379 1.28788 1.18415 1.09891 1.03423
## Proportion of Variance 0.3066 0.2135 0.08129 0.06379 0.05393 0.04645 0.04114
## Cumulative Proportion 0.3066 0.5201 0.60142 0.66521 0.71914 0.76559 0.80673
## PC8 PC9 PC10 PC11 PC12 PC13 PC14
## Standard deviation 0.99030 0.85552 0.75400 0.67049 0.65151 0.64104 0.57399
## Proportion of Variance 0.03772 0.02815 0.02187 0.01729 0.01633 0.01581 0.01267
## Cumulative Proportion 0.84445 0.87260 0.89447 0.91176 0.92808 0.94389 0.95656
## PC15 PC16 PC17 PC18 PC19 PC20 PC21
## Standard deviation 0.51590 0.49111 0.40960 0.40167 0.37014 0.26727 0.20134
## Proportion of Variance 0.01024 0.00928 0.00645 0.00621 0.00527 0.00275 0.00156
## Cumulative Proportion 0.96680 0.97607 0.98253 0.98873 0.99400 0.99675 0.99831
## PC22 PC23 PC24 PC25 PC26
## Standard deviation 0.14559 0.10740 0.07652 0.06000 0.04282
## Proportion of Variance 0.00082 0.00044 0.00023 0.00014 0.00007
## Cumulative Proportion 0.99912 0.99957 0.99979 0.99993 1.00000df_cluster <- as.data.frame(-pca2$x[, 1:2])
df_cluster## PC1 PC2
## Dion Waiters -1.87877434 2.893422978
## Buddy Hield -3.50692348 1.416444649
## Kentavious Caldwell-Pope -2.20137247 0.823482753
## Jalen Williams -1.77088011 1.662712781
## Dennis Smith Jr. -2.38981682 -4.058571848
## Ochai Agbaji -1.61424758 3.117399223
## Bradley Beal -0.82754947 -0.659943331
## Dereck Lively II 5.13940729 4.908354365
## Greg Monroe 0.61040396 -0.650297864
## Brandon Ingram -1.61026723 -1.916191506
## Mikal Bridges -1.14420389 3.554794904
## Derrick Williams 1.06807992 -1.073410109
## Ben McLemore 0.30783252 -0.245872619
## Anthony Edwards -1.36140263 -2.673735875
## Josh Jackson 1.57310640 -3.071593787
## Kira Lewis Jr. -3.22512852 -0.145447070
## Zach LaVine -1.55093364 2.642329927
## Aaron Nesmith -3.24624148 1.628776546
## Justise Winslow -0.32450165 -0.046720801
## Kevin Knox -0.57857116 -0.787030146
## Jarace Walker 1.37801187 1.216966913
## Noah Vonleh 1.16775866 -0.188969620
## Willie Cauley-Stein 2.67767592 4.026223965
## Michael Kidd-Gilchrist 1.54339794 -0.537228608
## Kemba Walker -3.16087458 -0.642457328
## Myles Turner 1.91803535 1.800563503
## Jett Howard -2.88030411 1.519978771
## Evan Turner -1.44633779 -0.509352088
## Domantas Sabonis 1.73763383 1.176518418
## Anthony Davis 6.81946695 -1.298925467
## Bennedict Mathurin -1.03736333 1.047637079
## D'Angelo Russell -2.92106171 -2.705140788
## Joel Embiid 4.56071760 0.275049579
## Steven Adams 3.71212322 2.800032293
## Chris Duarte -1.86046277 1.292904206
## Jayson Tatum -0.72039348 -2.664410902
## Kelly Olynyk 0.78055387 3.858127881
## Paolo Banchero 0.78117820 -2.804053329
## Paul George -2.04066776 0.155321706
## Jalen Duren 6.04834658 -0.567839906
## DeMarcus Cousins 4.47722765 -2.532951574
## De'Andre Hunter -0.73977851 2.045387990
## Jimmer Fredette -4.10068583 0.151455721
## Andrew Wiggins 0.40394987 -2.619085299
## Ziaire Williams -1.73956057 0.236909097
## Kendall Marshall -4.50594244 1.991570055
## Markieff Morris 1.34708852 3.999247075
## Deandre Ayton 6.42063201 -3.989709140
## Michael Carter-Williams -2.86581601 0.870790993
## John Wall -1.68160821 -3.975501776
## Kyrie Irving -3.69754992 -2.860691262
## Cason Wallace -2.27311780 0.159975101
## Stanley Johnson -1.12015696 -1.170691584
## Davion Mitchell -3.46328985 1.590099336
## Otto Porter Jr. 0.11050188 1.773914232
## Joshua Primo -1.51844668 3.740079647
## Anthony Black -1.15287196 -1.737863300
## Markelle Fultz -1.98389283 -6.289069268
## John Henson 3.00626308 3.180396627
## Donovan Mitchell -3.10251794 1.824734500
## Jabari Parker 2.47210802 -3.877981214
## Trey Burke -3.56814702 -0.621441435
## Wes Johnson 1.59432891 -1.355658714
## Jahlil Okafor 5.53352532 -2.780697880
## Andre Drummond 6.01362530 0.579810217
## Karl-Anthony Towns 2.12840084 1.109330782
## Marvin Bagley III 5.74422617 -4.591555606
## Tristan Thompson -4.06381690 2.262810456
## Ed Davis 3.34988242 3.186735688
## Marcus Smart -2.54328344 -2.015237976
## Doug McDermott -0.97886742 1.321678910
## Jalen Suggs -1.51193273 -1.416722416
## Julius Randle 2.24340507 -3.042945784
## Miles Bridges -0.43665877 0.849012232
## Zion Williamson 5.17455606 -4.695617011
## Johnny Davis -1.29838193 0.145475806
## Tyrese Haliburton -2.20631834 2.697991218
## Damian Lillard -4.26753179 -0.405236246
## Alex Len 3.65666018 2.221479147
## P.J. Washington 0.38430532 1.057924680
## Obi Toppin 3.10013537 0.850875977
## Jaren Jackson Jr. 1.54989689 1.654063788
## Trae Young -6.57771252 -7.880998042
## James Bouknight -1.50440966 0.805096400
## Patrick Patterson 2.59675241 2.071578251
## Anthony Bennett 3.16583380 -1.008475683
## Derrick Favors 5.07612783 -0.537442192
## Ja Morant -2.44350644 -3.154068779
## Cole Aldrich 2.80512022 3.923034303
## Bam Adebayo 6.03442637 -1.151787352
## Gordon Hayward -0.63712837 0.809277017
## Al-Farouq Aminu 1.06979323 -0.683184259
## Devin Booker -1.24174959 3.484009154
## Brandon Miller -1.94836905 -2.026234708
## Keegan Murray 1.65497692 0.671771098
## Tyler Herro -1.75186786 0.416344735
## Jabari Smith Jr. -1.57911124 -1.495506781
## Jamal Murray -2.32101496 -1.773731573
## Shai Gilgeous-Alexander -1.99930055 -2.359093850
## Trey Lyles 2.31079038 2.311364621
## Romeo Langford -0.69108021 -2.226529738
## Alec Burks -0.72391129 -2.047902304
## Marquese Chriss 2.85951816 -0.454169608
## Brandon Knight -3.48956878 -2.033951063
## Mo Bamba 5.19088002 -0.667639378
## Coby White -3.30501478 -0.962609638
## Xavier Henry -1.19039770 0.836284356
## Isaac Okoro 0.45583166 -0.276546204
## Nerlens Noel 5.52149230 -0.801447386
## Rui Hachimura 0.93351176 2.636481966
## Cameron Payne -4.43284878 -1.397687085
## Terrence Ross -1.12336118 2.567881207
## Frank Kaminsky -0.17667352 4.085571358
## Elfrid Payton -1.97980456 -0.903624480
## Harrison Barnes -1.07392023 0.415393391
## Patrick Williams -0.07429544 1.354172663
## Ben Simmons 3.63357550 -6.421547748
## Austin Rivers -2.94309828 -1.408832101
## Jeremy Sochan 0.94012964 1.962196272
## Jakob Poeltl 3.09307807 1.010869663
## T.J. Warren 1.14870807 -0.419904979
## Marcus Morris 0.78619850 2.586158338
## Cody Zeller 2.04375199 -0.560514715
## Taylor Hendricks 1.22550512 0.175647086
## Jerome Robinson -3.01145976 0.204910540
## Ekpe Udoh 2.95349083 -2.108737125
## Collin Sexton -2.49870233 -3.953433538
## Jaxson Hayes 6.14378844 1.935755954
## Kris Dunn -2.64831964 0.054392871
## De'Aaron Fox -0.82524429 -3.344308266
## Wendell Carter Jr. 3.43851631 -0.759548294
## Jonathan Isaac 1.55579355 0.429327692
## Gradey Dick -1.38567770 1.245442843
## Jaden Ivey -1.84082641 0.056522486
## Jordan Hawkins -3.10079731 2.711257786
## Jaylen Brown -0.83240004 -2.294511909
## Cameron Johnson -1.40190265 2.112733416
## Malik Monk -2.24035661 -1.712986893
## RJ Barrett -0.01227205 -5.110094647
## Thomas Robinson 0.46560196 2.442928178
## Cade Cunningham -2.85426045 -4.449564443
## Evan Mobley 4.34455690 -2.691116412
## Onyeka Okongwu 5.85709792 -2.599723240
## Chet Holmgren 4.24371197 -0.005887613
## Lauri Markkanen -0.13400874 0.115337390
## Luke Kennard -2.73577444 0.919701787
## Lonzo Ball -0.20427619 -0.082601232
## Scottie Barnes -0.34079523 -0.018038969
## Nik Stauskas -3.10769654 1.847553292
## Jarrett Culver -1.47235138 0.024087543
## Meyers Leonard 2.79244626 3.646226533
## Shabazz Muhammad 0.41381159 -2.159011494
## Moses Moody -1.43065318 -1.445225669
## Denzel Valentine -3.02166326 3.193070266
## Zach Collins 2.29766335 2.206084028
## Cam Reddish -4.08549065 0.100918560
## Jalen Smith 2.16080003 1.786709370
## CJ McCollum -3.87520407 -0.619831203
## Aaron Gordon 2.77173027 -0.539925608
## Jeremy Lamb -0.44879947 2.590888791
## Victor Oladipo -0.59336620 2.543388232
## Taurean Prince -1.41666106 3.713851811
## Franz Wagner -1.19544539 2.167513895
## Devin Vassell -0.68178092 4.075928100
## Klay Thompson -3.79667554 0.207371725fviz_nbclust(df_cluster, kmeans, method = 'wss')
fviz_nbclust(df_cluster, kmeans, method = 'silhouette')
fviz_nbclust(df_cluster, kmeans, method = 'gap_stat')
k <- 10
df_cbb.kmeans = kmeans(df_cluster, centers = k, nstart = 50)
fviz_cluster(df_cbb.kmeans, df_cluster, labelsize = 5, pointsize = 1, show.clust.cent = FALSE, repel = TRUE)
df_cbb.sample$group <- df_cbb.kmeans$cluster
df_cbb.sample <- df_cbb.sample |> relocate(group, .before = dunk_made)
head(df_cbb.sample)## group dunk_made dunk_attempts dunk_pct rim_made
## Dion Waiters 7 0.3661972 0.4084507 0.897 1.2676056
## Buddy Hield 8 0.1363636 0.1666667 0.818 0.9015152
## Kentavious Caldwell-Pope 8 0.2343750 0.2500000 0.938 0.9843750
## Jalen Williams 7 0.2976190 0.3214286 0.926 1.4761905
## Dennis Smith Jr. 1 0.6875000 0.6875000 1.000 3.2187500
## Ochai Agbaji 7 0.3032787 0.3360656 0.902 0.9754098
## rim_attempts rim_pct rim_asted other2pt_made
## Dion Waiters 2.028169 0.625 0.378 0.5492958
## Buddy Hield 1.348485 0.669 0.277 0.2651515
## Kentavious Caldwell-Pope 1.468750 0.670 0.349 0.6093750
## Jalen Williams 2.214286 0.667 0.355 0.6309524
## Dennis Smith Jr. 5.031250 0.640 0.155 1.0312500
## Ochai Agbaji 1.344262 0.726 0.529 0.2950820
## other2pt_attempts other2pt_pct other2pt_asted fg2_pct
## Dion Waiters 1.4084507 0.390 0.282 0.513
## Buddy Hield 0.7651515 0.347 0.057 0.508
## Kentavious Caldwell-Pope 1.7500000 0.348 0.385 0.504
## Jalen Williams 1.6071429 0.393 0.170 0.525
## Dennis Smith Jr. 3.3437500 0.308 0.030 0.509
## Ochai Agbaji 1.0327869 0.286 0.389 0.526
## fg3_per_g fg3a_per_g fg3_asted fg3_pct_per_g games
## Dion Waiters 1.0 2.8 0.780 0.3571429 71
## Buddy Hield 2.6 6.8 0.687 0.3823529 132
## Kentavious Caldwell-Pope 2.3 6.9 0.583 0.3333333 64
## Jalen Williams 1.1 3.0 0.714 0.3666667 84
## Dennis Smith Jr. 1.7 4.8 0.418 0.3541667 32
## Ochai Agbaji 2.0 5.5 0.854 0.3636364 122
## ft_per_g fta_per_g ast_per_g_college orb_per_g
## Dion Waiters 1.8 2.3 2.0 0.4
## Buddy Hield 2.8 3.4 1.9 1.6
## Kentavious Caldwell-Pope 2.9 3.9 1.5 1.3
## Jalen Williams 2.6 3.3 2.9 0.5
## Dennis Smith Jr. 4.5 6.3 6.2 0.8
## Ochai Agbaji 1.8 2.5 1.6 1.0
## drb_per_g stl_per_g blk_per_g tov_per_g
## Dion Waiters 1.5 1.5 0.2 1.1
## Buddy Hield 3.4 1.3 0.3 2.2
## Kentavious Caldwell-Pope 4.8 1.9 0.4 1.6
## Jalen Williams 3.2 1.2 0.5 1.6
## Dennis Smith Jr. 3.8 1.9 0.4 3.4
## Ochai Agbaji 3.4 1.0 0.5 1.7
## pts_per_g_college
## Dion Waiters 9.7
## Buddy Hield 17.4
## Kentavious Caldwell-Pope 15.8
## Jalen Williams 12.6
## Dennis Smith Jr. 18.1
## Ochai Agbaji 13.5df_cbb.sample |> group_by(group) |>
summarize(across(everything(), mean))## # A tibble: 10 × 27
## group dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0.414 0.457 0.869 2.38 3.82 0.626
## 2 2 0.28 0.3 0.466 3.06 5.44 0.570
## 3 3 2.03 2.15 0.945 5.13 6.72 0.764
## 4 4 0.464 0.510 0.917 1.64 2.46 0.671
## 5 5 1.13 1.23 0.918 3.19 4.80 0.669
## 6 6 0.927 1.01 0.925 2.59 3.74 0.695
## 7 7 0.271 0.306 0.890 1.03 1.55 0.674
## 8 8 0.127 0.145 0.830 0.973 1.58 0.616
## 9 9 0.644 0.695 0.922 1.56 2.12 0.745
## 10 10 2.06 2.16 0.959 3.51 4.49 0.787
## # ℹ 20 more variables: rim_asted <dbl>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <dbl>, other2pt_asted <dbl>,
## # fg2_pct <dbl>, fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_asted <dbl>,
## # fg3_pct_per_g <dbl>, games <dbl>, ft_per_g <dbl>, fta_per_g <dbl>,
## # ast_per_g_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, stl_per_g <dbl>,
## # blk_per_g <dbl>, tov_per_g <dbl>, pts_per_g_college <dbl>df_cbb.sample |> group_by(group) |>
summarize(across(everything(), median))## # A tibble: 10 × 27
## group dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0.412 0.438 0.909 2.41 4 0.634
## 2 2 0.28 0.3 0.466 3.06 5.44 0.570
## 3 3 1.99 2.09 0.953 5.05 6.65 0.774
## 4 4 0.465 0.493 0.917 1.59 2.48 0.666
## 5 5 1.04 1.12 0.925 3.28 4.87 0.673
## 6 6 0.85 0.9 0.933 2.59 3.66 0.699
## 7 7 0.283 0.294 0.901 1.03 1.45 0.684
## 8 8 0.128 0.137 0.934 0.963 1.54 0.613
## 9 9 0.569 0.667 0.939 1.68 2.22 0.748
## 10 10 2.24 2.31 0.958 3.63 4.56 0.776
## # ℹ 20 more variables: rim_asted <dbl>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <dbl>, other2pt_asted <dbl>,
## # fg2_pct <dbl>, fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_asted <dbl>,
## # fg3_pct_per_g <dbl>, games <dbl>, ft_per_g <dbl>, fta_per_g <dbl>,
## # ast_per_g_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, stl_per_g <dbl>,
## # blk_per_g <dbl>, tov_per_g <dbl>, pts_per_g_college <dbl>